Latent Cardiometabolic Phenotypes and Their Sociodemographic Correlates Among US Adults: A Cross‐Sectional Latent Class Analysis Using NHANES (2011–2018) Data

Aim The study aims to identify distinct cardiometabolic phenotypes based on multiple metabolic risk indicators, assess their associations with sociodemographic characteristics, and evaluate income‐related differences through cardiometabolic risk using decomposition methods to quantify the contribution of observed factors to these disparities. Materials and Methods A cross‐sectional analytical study was conducted using National Health and Nutrition Examination Survey (NHANES) 2011–2018 data. Adults aged 20 years and above with complete cardiometabolic biomarker data were included. Six cardiometabolic indicators including waist circumference, systolic blood pressure, fasting plasma glucose, glycated haemoglobin, triglycerides, and high‐density lipoprotein cholesterol were used to derive latent phenotypes using latent class analysis. Multinomial logistic regression examined associations between sociodemographic factors and phenotype membership. Oaxaca‐Blinder decomposition analysis assessed income‐related disparities in the high cardiometabolic risk phenotype. Results A total of 7733 participants were included in the analysis. A three class model demonstrated optimal fit and identified metabolically healthy (54.9%), hypertension‐hyperglycemia (33.1%) and high cardiometabolic risk (11.9%) phenotypes. The high‐risk phenotype exhibited elevated probabilities of obesity, dyslipidemia and glycaemic abnormalities. Increasing age, male sex, lower income and lower educational attainment were significantly associated with high risk phenotype membership. Decomposition analysis demonstrated modest income‐related disparities, with waist circumference and education contributing the largest explained components. Conclusion Cardiometabolic risk among U.S. adults clusters into three distinct phenotypes associated with sociodemographic and metabolic determinants. These findings highlight the need for integrated strategies targeting early metabolic dysregulation and broader social determinants of health.